I'm not too impressed with this article since it doesn't really give a definition for computing, just picks a few similarities between what we see as computing (in the practical sense) and what cells do.

It's a shame because there *has* been a lot of deep work done on what kind of computer life is. People often use the Chomsky Hierarchy (https://en.wikipedia.org/wiki/Chomsky_hierarchy) to define the different types of computer vs automata. Importantly, a classical Turing machine is Type-0 on the Chomsky Hierarchy. Depending on what parts you include from a biological system, you could argue it's anywhere from Type-0 to Type-4.

Interestingly, the PhD thesis of well-known geneticist Aviv Regev was to show that certain combinations of enzymes with chemical concentration states are enough to emulate pi-calculus, and therefore are Turing machines! https://psb.stanford.edu/psb-online/proceedings/psb01/regev....

This is the kind of evolved computer science that was going on when I was a teenager. Have an upvote eig!

My addition: it's funny for how much speculation we get in the, "hard cognitive science" (RIP) that in lieu of the big insights we get from Godel, Turing, Russell that many/most undergraduates and even post-graduates still haven't internalized Wittgenstein's work especially the Tractatus. I feel like it gets us to, "the questions you're asking about how life works and the questions about what is at the core of logic and mathematics (language) are definitely related but not in any of the fundamental ways you hope they are..."

For the uninitiated-- try reading the thing in one sitting. It takes about an hour:

https://wittgensteinproject.org/w/index.php/Tractatus_Logico...

The Aviv Regev paper you link was recently recommended to me as a useful reference for something. It was a nice surprise to see that Regev's thesis advisor was Ehud Shapiro, known to the Prolog community from his co-authorship of one of the good Prolog books (The Art of Prolog, with Leon Sterling - https://mitpress.mit.edu/9780262691635/the-art-of-prolog/). Indeed, Regev's thesis (and the paper above) propose a system based on a Flat Concurrent Prolog.

Shapiro was also the author of one of the two PhD theses that were a major influence to Inductive Logic Programming, a field at the intersection of logic programming and machine learning.

A lot of the kind of "deep work" you mention used to be done in the logic programming and ILP community in times past, before everyone seemingly switched to neural nets and statistical machine learning.